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Reinforcement learning for crop management support: Review, prospects and challenges

Authors
  • Gautron, Romain
  • Maillard, Odalric-Ambrym
  • Preux, Philippe
  • Corbeels, Marc
  • Sabbadin, Régis
Publication Date
Jan 01, 2022
Source
Agritrop
Keywords
Language
English
License
Unknown
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Abstract

Reinforcement learning (RL), including multi-armed bandits, is a branch of machine learning that deals with the problem of sequential decision-making in uncertain and unknown environments through learning by practice. While best known for being the core of the artificial intelligence (AI) world's best Go game player, RL has a vast range of potential applications. RL may help to address some of the criticisms leveled against crop management decision support systems (DSS): it is an interactive, geared towards action, contextual tool to evaluate series of crop operations faced with uncertainties. A review of RL use for crop management DSS reveals a limited number of contributions. We profile key prospects for a human-centered, real-world, interactive RL-based system to face tomorrow's agricultural decisions, and theoretical and ongoing practical challenges that may explain its current low uptake. We argue that a joint research effort from the RL and agronomy communities is necessary to explore RL's full potential.

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